Machine Learning 1
- The course aims to help students develop an understanding of the process to learn from data, familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
- Build features suitable for the selected machine learning models
- Build and interpret the data visualizations in Python and R programming language
- Construct machine learning models on the proposed data sets in R
- Evaluate performance of the models
- Tune models to improve prediction and classification performance of the models
- Math Essentials. Intro to Python in Google Colab
- Intro to Statistical learning
- Simple Linear Regression (SLR)
- Multiple Linear Regression (MLR), kNN
- Classification: Logistic Regression
- Classification: LDA, QDA, KNN
- Resampling methods. CV, Bootstrap
- Linear model selection & regularization
- Non-linear regression
- Decision Trees
- Bagging, Random Forest, Boosting
- Support Vector Machines/Classifiers
- QuizzesВсе вопросы на английском языке.
- homework assignments
- ExamThere will be exams at the end of each of the 4 modules. The examination locations are TBD. An in-class exam is closed book, notes, calculators and phones. Take-home exam is an open book/internet, but no collaboration. Exam questions are different from homework questions: HW deepens your understanding, but the exams measure it. Each exam is cumulative.
- Coursework Project (CP) in R programming languageAdministered by LSE/UoL
- TestsThere will be tests at the end of each of the 4 modules. The examination locations are TBD. An in-class test is closed book, notes, calculators and phones. Take-home test is an open book/internet, but no collaboration. Test questions are different from homework questions: HW deepens your understanding, but the tests measure it. Each test is cumulative. Do not book travel that conflicts with this date.
- Interim assessment (1 module)0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5* Exam
- Interim assessment (4 module)0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*(Module1 + Test2 + Test3 +2*UOL Results)
- Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008